Download build/webgpu/test.json from webgpu-kernels/ai.onnx.Atanh: direct link, hf CLI and curl.
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hf download hf://webgpu-kernels/ai.onnx.Atanh@v1/build/webgpu/test.json
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curl -L -o test.json https://huggingface.co/kernels/webgpu-kernels/ai.onnx.Atanh/resolve/v1/build/webgpu/test.json
11 kB
| { | |
| "cases": [ | |
| { | |
| "name": "f32_values", | |
| "inputs": { | |
| "x": { | |
| "dtype": "float32", | |
| "shape": [6], | |
| "data": { "kind": "values", "values": [-0.9, -0.5, 0.0, 0.25, 0.5, 0.9] } | |
| } | |
| }, | |
| "outputs": { "y": { "dtype": "float32", "shape": [6], "tolerance": 0.000001 } } | |
| }, | |
| { | |
| "name": "f32_subnormal_identity_tail_gpu_gap", | |
| "skipGpu": { | |
| "category": "permanent", | |
| "reason": "Portable WGSL floating-point semantics do not guarantee preservation of the subnormal values required by this fixture. Backend evidence: WebGPU/Metal flushes subnormals to zero (f32 and f16); the kernel cannot preserve denormal inputs/outputs bit-exactly." | |
| }, | |
| "provenance": { | |
| "source": "onnxruntime/test/providers/cpu/math/element_wise_ops_test.cc", | |
| "test": "MathOpTest.Atanh", | |
| "notes": "For tiny finite inputs atanh(x) rounds back to x in float32; zero-flushing erases the signed tail." | |
| }, | |
| "inputs": { | |
| "x": { | |
| "dtype": "float32", | |
| "shape": [4], | |
| "data": { "kind": "values", "values": [-1e-39, -1e-40, 1e-40, 1e-39] } | |
| } | |
| }, | |
| "outputs": { "y": { "dtype": "float32", "shape": [4], "tolerance": 0 } } | |
| }, | |
| { | |
| "name": "f32_near_unit_boundary_accuracy_gpu_gap", | |
| "skipGpu": { | |
| "category": "todo", | |
| "reason": "Near the high-slope domain boundary, the package's f32 result differs from the correctly rounded expected value by about one ULP. A higher-accuracy algorithm or software-extended precision could close the gap." | |
| }, | |
| "provenance": { | |
| "source": "onnxruntime/test/providers/cpu/math/element_wise_ops_test.cc", | |
| "test": "MathOpTest.Atanh", | |
| "notes": "Values just inside the open Atanh domain are finite but numerically sensitive, distinguishing a rounded finite result from an incorrect Inf or NaN classification." | |
| }, | |
| "inputs": { | |
| "x": { | |
| "dtype": "float32", | |
| "shape": [4], | |
| "data": { | |
| "kind": "values", | |
| "values": [-0.9999999403953552, -0.9999998807907104, 0.9999998807907104, 0.9999999403953552] | |
| } | |
| } | |
| }, | |
| "outputs": { "y": { "dtype": "float32", "shape": [4], "tolerance": 5e-7, "relTolerance": 0 } } | |
| }, | |
| { | |
| "name": "f32_subnormal_identity_tail_scalar_gpu_gap", | |
| "skipGpu": { | |
| "category": "permanent", | |
| "reason": "Portable WGSL floating-point semantics do not guarantee preservation of the subnormal values required by this fixture. Backend evidence: WebGPU/Metal flushes subnormals to zero (f32 and f16); the kernel cannot preserve denormal inputs/outputs bit-exactly." | |
| }, | |
| "provenance": { | |
| "source": "onnxruntime/test/providers/cpu/math/element_wise_ops_test.cc", | |
| "test": "MathOpTest.Atanh", | |
| "notes": "Unaligned scalar-path subnormal inputs have valid finite Atanh outputs." | |
| }, | |
| "inputs": { | |
| "x": { "dtype": "float32", "shape": [3], "data": { "kind": "values", "values": [-1e-40, 0.0, 1e-40] } } | |
| }, | |
| "outputs": { "y": { "dtype": "float32", "shape": [3], "tolerance": 0 } } | |
| }, | |
| { | |
| "name": "f32_near_unit_boundary_accuracy_scalar_gpu_gap", | |
| "skipGpu": { | |
| "category": "todo", | |
| "reason": "Near the high-slope domain boundary, the package's scalar f32 result differs from the correctly rounded expected value by about one ULP. A higher-accuracy algorithm or software-extended precision could close the gap." | |
| }, | |
| "provenance": { | |
| "source": "onnxruntime/test/providers/cpu/math/element_wise_ops_test.cc", | |
| "test": "MathOpTest.Atanh", | |
| "notes": "Unaligned scalar-path values just inside the open Atanh domain exercise finite near-boundary accuracy." | |
| }, | |
| "inputs": { | |
| "x": { | |
| "dtype": "float32", | |
| "shape": [3], | |
| "data": { "kind": "values", "values": [-0.9999999403953552, 0.9999998807907104, 0.9999999403953552] } | |
| } | |
| }, | |
| "outputs": { "y": { "dtype": "float32", "shape": [3], "tolerance": 5e-7, "relTolerance": 0 } } | |
| }, | |
| { | |
| "name": "f16_values", | |
| "inputs": { | |
| "x": { | |
| "dtype": "float16", | |
| "shape": [2, 3], | |
| "data": { "kind": "values", "values": [-0.9, -0.5, 0.0, 0.25, 0.5, 0.9] } | |
| } | |
| }, | |
| "outputs": { "y": { "dtype": "float16", "shape": [2, 3] } }, | |
| "tolerance": 0.003 | |
| }, | |
| { | |
| "name": "out_of_domain_returns_nan", | |
| "inputs": { | |
| "x": { | |
| "dtype": "float32", | |
| "shape": [6], | |
| "data": { "kind": "values", "values": [1.5, -1.5, 2.0, -2.0, 1.0, -1.0] } | |
| } | |
| }, | |
| "outputs": { "y": { "dtype": "float32", "shape": [6], "tolerance": 0.000001, "allowNaN": true } } | |
| }, | |
| { | |
| "name": "f16_domain_and_nonfinite_values", | |
| "inputs": { | |
| "x": { | |
| "dtype": "float16", | |
| "shape": [10], | |
| "data": { "kind": "values", "values": [-1.0, -0.999, 0.0, 0.0, 0.999, 1.0, 1.001, -1.001, "Infinity", "NaN"] } | |
| } | |
| }, | |
| "outputs": { "y": { "dtype": "float16", "shape": [10], "tolerance": 0.003, "allowNaN": true } } | |
| }, | |
| { | |
| "name": "ort_float_boundary_interval", | |
| "provenance": { | |
| "source": "onnxruntime/test/providers/cpu/math/element_wise_ops_test.cc", | |
| "test": "MathOpTest.Atanh" | |
| }, | |
| "inputs": { | |
| "x": { "dtype": "float32", "shape": [5], "data": { "kind": "values", "values": [-1.0, -0.5, 0.0, 0.5, 1.0] } } | |
| }, | |
| "outputs": { "y": { "dtype": "float32", "shape": [5], "tolerance": 0.00001 } } | |
| }, | |
| { | |
| "name": "ort_domain_and_nonfinite_values", | |
| "provenance": { | |
| "source": "onnxruntime/test/providers/cpu/math/element_wise_ops_test.cc", | |
| "test": "MathOpTest.Atanh", | |
| "notes": "Adds endpoints, out-of-domain values, signed infinities, signed zero, and NaN." | |
| }, | |
| "inputs": { | |
| "x": { | |
| "dtype": "float32", | |
| "shape": [11], | |
| "data": { | |
| "kind": "values", | |
| "values": [-1.0, -0.9999999, 0.0, 0.0, 0.9999999, 1.0, 1.0000001, -1.0000001, "Infinity", "-Infinity", "NaN"] | |
| } | |
| } | |
| }, | |
| "outputs": { "y": { "dtype": "float32", "shape": [11], "tolerance": 0.00001, "allowNaN": true } } | |
| }, | |
| { | |
| "name": "onnx_backend_example", | |
| "provenance": { "source": "cmake/external/onnx/onnx/backend/test/data/node/test_atanh_example" }, | |
| "inputs": { "x": { "dtype": "float32", "shape": [3], "data": { "kind": "values", "values": [-0.5, 0.0, 0.5] } } }, | |
| "outputs": { "y": { "dtype": "float32", "shape": [3], "tolerance": 0.000001 } } | |
| }, | |
| { | |
| "name": "onnx_backend_atanh", | |
| "provenance": { "source": "cmake/external/onnx/onnx/backend/test/data/node/test_atanh" }, | |
| "inputs": { | |
| "x": { | |
| "dtype": "float32", | |
| "shape": [3, 4, 5], | |
| "data": { | |
| "kind": "values", | |
| "values": [0.54881352186203, 0.7151893377304077, 0.6027633547782898, 0.5448831915855408, 0.42365479469299316, 0.6458941102027893, 0.4375872015953064, 0.891772985458374, 0.9636627435684204, 0.3834415078163147, 0.7917250394821167, 0.5288949012756348, 0.5680445432662964, 0.9255966544151306, 0.07103605568408966, 0.08712930232286453, 0.020218396559357643, 0.832619845867157, 0.7781567573547363, 0.8700121641159058, 0.978618323802948, 0.7991585731506348, 0.4614793658256531, 0.7805292010307312, 0.11827442795038223, 0.6399210095405579, 0.14335328340530396, 0.9446688890457153, 0.5218483209609985, 0.4146619439125061, 0.26455560326576233, 0.7742336988449097, 0.4561503231525421, 0.568433940410614, 0.018789799883961678, 0.6176354885101318, 0.6120957136154175, 0.6169340014457703, 0.9437480568885803, 0.681820273399353, 0.35950788855552673, 0.43703195452690125, 0.6976311802864075, 0.0602254718542099, 0.6667667031288147, 0.670637845993042, 0.21038256585597992, 0.12892629206180573, 0.31542834639549255, 0.36371076107025146, 0.5701967477798462, 0.4386015236377716, 0.9883738160133362, 0.10204481333494186, 0.20887675881385803, 0.16130951046943665, 0.6531082987785339, 0.25329160690307617, 0.4663107693195343, 0.24442559480667114] | |
| } | |
| } | |
| }, | |
| "outputs": { "y": { "dtype": "float32", "shape": [3, 4, 5], "tolerance": 0.00001, "allowNaN": true } } | |
| }, | |
| { | |
| "name": "onnx_backend_atanh_example", | |
| "provenance": { "source": "cmake/external/onnx/onnx/backend/test/data/node/test_atanh_example" }, | |
| "inputs": { "x": { "dtype": "float32", "shape": [3], "data": { "kind": "values", "values": [-0.5, 0.0, 0.5] } } }, | |
| "outputs": { "y": { "dtype": "float32", "shape": [3], "tolerance": 0.00001 } } | |
| }, | |
| { | |
| "name": "vec4_f16_lanes", | |
| "inputs": { | |
| "x": { | |
| "dtype": "float16", | |
| "shape": [16], | |
| "data": { | |
| "kind": "values", | |
| "values": [-0.95, -0.9, -0.75, -0.5, -0.25, -0.125, 0.0, 0.0625, 0.125, 0.25, 0.375, 0.5, 0.625, 0.75, 0.875, 0.95] | |
| } | |
| } | |
| }, | |
| "outputs": { "y": { "dtype": "float16", "shape": [16], "tolerance": 0.001, "relTolerance": 0.005 } } | |
| }, | |
| { | |
| "name": "empty_input_zero_dim", | |
| "inputs": { "x": { "dtype": "float32", "shape": [0], "data": { "kind": "values", "values": [] } } }, | |
| "outputs": { "y": { "dtype": "float32", "shape": [0], "tolerance": 0 } } | |
| }, | |
| { | |
| "name": "f32_near_unit_boundary_gpu_runnable", | |
| "inputs": { | |
| "x": { | |
| "dtype": "float32", | |
| "shape": [6], | |
| "data": { "kind": "values", "values": [-0.9999, -0.999, -0.99, 0.99, 0.999, 0.9999] } | |
| } | |
| }, | |
| "outputs": { "y": { "dtype": "float32", "shape": [6], "tolerance": 0.001, "relTolerance": 0.0001 } } | |
| }, | |
| { | |
| "name": "rank0_scalar", | |
| "inputs": { "x": { "dtype": "float32", "shape": [], "data": { "kind": "values", "values": [0.5] } } }, | |
| "outputs": { | |
| "y": { | |
| "dtype": "float32", | |
| "shape": [], | |
| "data": { "kind": "values", "values": [0.5493061443340549] }, | |
| "tolerance": 0.000001 | |
| } | |
| } | |
| }, | |
| { | |
| "name": "f32_boundary_exact_one_and_neg_one", | |
| "inputs": { | |
| "x": { "dtype": "float32", "shape": [4], "data": { "kind": "values", "values": [-1.0, -0.5, 0.5, 1.0] } } | |
| }, | |
| "outputs": { "y": { "dtype": "float32", "shape": [4], "tolerance": 0.000001, "allowNaN": true } } | |
| }, | |
| { | |
| "name": "f16_empty_zero_numel_shape", | |
| "inputs": { "x": { "dtype": "float16", "shape": [0], "data": { "kind": "values", "values": [] } } }, | |
| "outputs": { "y": { "dtype": "float16", "shape": [0] } } | |
| }, | |
| { | |
| "name": "f32_vec4_sustained_1024", | |
| "inputs": { | |
| "x": { "dtype": "float32", "shape": [1024], "data": { "kind": "linspace", "start": -0.9, "end": 0.9 } } | |
| }, | |
| "outputs": { "y": { "dtype": "float32", "shape": [1024], "tolerance": 0.00001 } } | |
| } | |
| ] | |
| } | |